Metadata-Version: 2.1
Name: lumod
Version: 0.1.1.1
Summary: Hydrological Lumped Model Tools
Home-page: https://gitlab.com/Zaul_AE/lumod
License: GNU-3
Author: Saul Arciniega Esparza
Author-email: zaul_ae@hotmail.com
Requires-Python: >=3.7,<4.0
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: matplotlib
Requires-Dist: numba
Requires-Dist: numpy
Requires-Dist: pandas
Project-URL: Documentation, https://zaul_ae.gitlab.io/lumod-docs
Project-URL: Repository, https://gitlab.com/Zaul_AE/lumod
Description-Content-Type: text/markdown

# **Lumped Models (LuMod) for Hydrology**

**LuMod** is an easy to use set of Lumped Models for hydrological simulation in Python language.

Compared with other source codes that pretend to be fast, **LuMod** was created to facilitate the modeling and the results processing. Moreover, some features of **LuMod** are compiled with [numba](http://numba.pydata.org/) to increase simulation speed.


## **Documentation**

Full documentation and examples are available here: [LuMod-Docs](https://zaul_ae.gitlab.io/lumod-docs)

Installation is available from PyPI: [LuMod project](https://pypi.org/project/lumod/)

## **Key Features**

**LuMod** incorporates well know hydrological models with different structures:

* **MILC**: One layer Lumped version of the MISDc model adapted for continous daily simulation.
* **HYMOD**: Rainfall-Runoff Model based on the Probability-Distributed Model concept that runs at daily timestep.
* **HBV**: Modified version of the Hydrologiska Byråns Vattenbalansavdelning (HBV) model.
* **GR4J**: Well known daily rain-runoff model that depends of four parameters.
* **GR2M**: Monthly rain-runoff model that depends of two parameters.
* **GR1A**: One parameter annual rain-runoff model.
* **MonteCarlo**: Random-based simulation for parameters optimization and uncertainty analysis.

## **Quick installation**

The easier way to install **LuMod** is using [PyPI](https://pypi.org/):

```bash
pip install lumod
```

## **Basic Example**

**LuMod** was thought to be easy to use and compatible with libraries dedicated to DataScience, so it works with Pandas and Matplotlib to facilitate the analysis of simulations.

```python
# Import modules
import pandas as pd
import matplotlib.pyplot as plt
import lumod
from lumod import tools

# Load example data
info, forcings = lumod.load_example(2)

# Create a model
model = lumod.models.GR4J(area=info.area, lat=info.lat)
print(model)

# Run your model
simulations = model.run(forcings)

# Validate your model
tools.plots.model_evaluation(forcings.prec, forcings.qt, simulations.qt)
```

## **Citation**

*Coming Soon*

## **Author**

**Main Developer**

Saúl Arciniega Esparza, Ph.D., Full Time Associate Professor at the [Faculty of Engineering](https://www.ingenieria.unam.mx/) at the [National Autonomous University of Mexico](https://www.unam.mx/), working on the [Hydrogeology Group](https://www.ingenieria.unam.mx/hydrogeology/).

[LinkedIn](https://www.linkedin.com/in/saularciniegaesparza/) | [Twitter](https://twitter.com/zaul_arciniega) | [ResearchGate](https://www.researchgate.net/profile/Saul-Arciniega-Esparza)


